The recent histologic subtyping of lung adenocarcinoma has demonstrated the prognostic values of histologic patterns in this malignancy. However, the histological features of lung squamous cell carcinoma (SCC) are much less established. This short review discusses several promising histological prognostic markers for SCC, including tumor budding, tumor cell nesting, and the spreading of tumors through air spaces. Wherever appropriate, the biological significance of these morphological features was also discussed. The investigators consider that histological prognostic markers are highly valuable in understanding the cancer biology of SCC, and in guiding clinical treatment. However, larger clinical cohorts are needed to better establish the prognostic values of the aforementioned histological markers. The application of modern technologies, including machine-learning, would make the histological analysis accurate and reproducible.
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http://dx.doi.org/10.14218/erhm.2021.00071 | DOI Listing |
J Cutan Pathol
January 2025
Division of Dermatology, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA.
Pemetrexed is a chemotherapeutic, antimetabolite agent that has been used in oncology to treat diseases such as metastatic non-small cell lung cancer and unresectable malignant pleural mesothelioma. Pemetrexed use may result in pseudocellulitis, which presents as poorly demarcated patches or plaques with erythema, edema, warmth, and tenderness. These lesions can present unilaterally or bilaterally on the lower extremities.
View Article and Find Full Text PDFCancer Control
January 2025
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, P.R. China.
Purpose: Splenic metastases (SM) from breast cancer (SMBC) are exceedingly rare. To date, the relevant literature is primarily based on pan-tumour species, with only a few studies exploring SM specifically in relation to breast cancer. As such, the present retrospective study explored the clinicopathological characteristics and prognoses of patients with SMBC at the breast care centre of the authors' hospital.
View Article and Find Full Text PDFJ Dent Res
January 2025
Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Missing teeth have been linked to incident cardiovascular disease, diabetes, and all-cause mortality. Our previous study revealed that signs of oral infections and inflammatory conditions (i.e.
View Article and Find Full Text PDFCancer Cell
December 2024
Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA. Electronic address:
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Software Engineering, University of Engineering and Technology-Taxila, 47050, Punjab, Pakistan. Electronic address:
Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection.
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